This tutorial shows how types are created using the creator and initialized using the toolbox.
The provided :class:`~deap.base.Fitness` class is an abstract class that needs a :attr:`~deap.base.Fitness.weights` attribute in order to be functional. A minimizing fitness is built using negatives weights. For example, the following line creates, in the :mod:`~deap.creator`, a ready to use single objective minimizing fitness named :class:`FitnessMin`.
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
The :attr:`~deap.base.Fitness.weights` argument must be a tuple so that multi
objective and single objective fitnesses can be treated the same way. A
:class:`FitnessMulti` would be created the same way but using weights=(1.0,
-1.0)
rendering a fitness that maximize the first objective and minimize the
second one. The weights can also be used to variate the importance of each
objective one against another. This means that the weights can be any real
number and only the sign is used to determine if a maximization of
minimization is done. An example of where the weights can be useful is in the
crowding distance sort made in the NSGA-II selection algorithm.
Simply by thinking of the different flavours of evolutionary algorithms (GA, GP, ES, PSO, DE, ...), we notice that an extremely large variety of individuals are possible. Here is a guide on how to create some of those individuals using the :mod:`~deap.creator` and initializing them using a :class:`~deap.base.Toolbox`.
The first individual created will be a simple list containing floats. In order
to produce this kind of individual, we need to create an
:class:`Individual` class, using the creator, that will inherit from the
standard :class:`list` and have a :attr:`fitness` attribute. Then we will
initialize this list using the :func:`~deap.tools.initRepeat` helper function
that will repeat n
times the float generator that has been registered
under the :func:`attr_float` alias of the toolbox. Note that the
:func:`attr_float` is a direct reference to the :func:`~random.random`
function.
from deap import base from deap import creator from deap import tools import random creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_float", random.random) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=IND_SIZE)
Calling :func:`toolbox.individual` will readily return a complete individual
composed of IND_SIZE
floating point numbers with a maximizing single
objective fitness attribute.
An individual for the permutation representation is almost similar to the
general list individual. In fact they both inherit from the basic
:class:`list` type. The only difference is that instead of filling the list
with a series of floats, we need to generate a random permutation and provide
that permutation to the individual. First, the individual class is created the
exact same way as the previous one. Then, an :func:`indices` function is added
to the toolbox referring to the :func:`~random.sample` function, sample is
used instead of :func:`~random.shuffle` because this last one does not return
the shuffled list. The indices function returns a complete permutation of the
numbers between 0
and IND_SIZE - 1
. Finally, the individual is
initialized with the :func:`~deap.tools.initIterate` function which gives to
the individual an iterable of what is produced by the call to the indices
function.
from deap import base from deap import creator from deap import tools import random creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("indices", random.sample, range(IND_SIZE), IND_SIZE) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.indices)
Calling :func:`toolbox.individual` will readily return a complete individual
that is a permutation of the integers 0
to IND_SIZE
with a minimizing
single objective fitness attribute.
The next individual that is commonly used is a prefix tree of mathematical
expressions. This time a :class:`~deap.gp.PrimitiveSet` must be defined
containing all possible mathematical operators that our individual can use.
Here the set is called MAIN
and has a single variable defined by the
arity. Operators :func:`~operator.add`, :func:`~operator.sub`, and
:func:`~operator.mul` are added to the primitive set with each an arity of 2.
Next, the :class:`Individual` class is created as before but having an
additional static attribute :attr:`pset` set to remember the global primitive
set. This time the content of the individuals will be generated by the
:func:`~deap.gp.genRamped` function that generate trees in a list format based
on a ramped procedure. Once again, the individual is initialised using the
:func:`~deap.tools.initIterate` function to give the complete generated
iterable to the individual class.
from deap import base from deap import creator from deap import gp from deap import tools import operator pset = gp.PrimitiveSet("MAIN", arity=1) pset.addPrimitive(operator.add, 2) pset.addPrimitive(operator.sub, 2) pset.addPrimitive(operator.mul, 2) creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin, pset=pset) toolbox = base.Toolbox() toolbox.register("expr", gp.genRamped, pset=pset, min_=1, max_=2) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
Calling :func:`toolbox.individual` will readily return a complete individual that is an arithmetic expression in the form of a prefix tree with a minimizing single objective fitness attribute.
Evolution strategies individuals are slightly different as they contain generally two list, one for the actual individual and one for its mutation parameters. This time instead of using the list base class we will inherit from an :class:`array.array` for both the individual and the strategy. Since there is no helper function to generate two different vectors in a single object we must define this function our-self. The :func:`initES` function receives two classes and instantiate them generating itself the random numbers in the intervals provided for individuals of a given size.
from deap import base from deap import creator from deap import tools import array import random creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode="d", fitness=creator.FitnessMin, strategy=None) creator.create("Strategy", array.array, typecode="d") def initES(icls, scls, size, imin, imax, smin, smax): ind = icls(random.uniform(imin, imax) for _ in range(size)) ind.strategy = scls(random.uniform(smin, smax) for _ in range(size)) return ind toolbox = base.Toolbox() toolbox.register("individual", initES, creator.Individual, creator.Strategy, IND_SIZE, MIN_VALUE, MAX_VALUE, MIN_STRATEGY, MAX_STRATEGY)
Calling :func:`toolbox.individual` will readily return a complete evolution strategy with a strategy vector and a minimizing single objective fitness attribute.
A particle is another special type of individual as it usually has a speed and generally remember its best position. This type of individual is created (once again) the same way inheriting from a list. This time :attr:`speed`, :attr:`best` and speed limits attributes are added to the object. Again, an initialization function :func:`initParticle` is also registered to produce the individual receiving the particle class, size, domain, and speed limits as arguments.
from deap import base from deap import creator from deap import tools import random creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Particle", list, fitness=creator.FitnessMax, speed=None, smin=None, smax=None, best=None) def initParticle(pcls, size, pmin, pmax, smin, smax): part = pcls(random.uniform(pmin, pmax) for _ in xrange(size)) part.speed = [random.uniform(smin, smax) for _ in xrange(size)] part.smin = smin part.smax = smax return part toolbox = base.Toolbox() toolbox.register("particle", initParticle, creator.Particle, size=2, pmin=-6, pmax=6, smin=-3, smax=3)
Calling :func:`toolbox.individual` will readily return a complete particle with a speed vector and a maximizing two objectives fitness attribute.
Supposing your problem have very specific needs. It is also possible to build custom individuals very easily. The next individual created is a list of alternating integers and floating point numbers, using the :func:`~deap.tools.initCycle` function.
from deap import base from deap import creator from deap import tools import random creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_int", random.randint, INT_MIN, INT_MAX) toolbox.register("attr_flt", random.uniform, FLT_MIN, FLT_MAX) toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_int, toolbox.attr_flt), n=N_CYCLES)
Calling :func:`toolbox.individual` will readily return a complete individual
of the form [int float int float ... int float]
with a maximizing two
objectives fitness attribute.
Population are much like individuals, instead of being initialized with attributes it is filled with individuals, strategies or particles.
A bag population is the most commonly used type, it has no particular ordering although it is generally implemented using a list. Since the bag has no particular attribute it does not need any special class. The population is initialized using directly the toolbox and the :func:`~deap.tools.initRepeat` function.
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
Calling :func:`toolbox.population` will readily return a complete population in a list providing a number of times the repeat helper must be repeated as an argument of the population function.
A grid population is a special case of structured population where neighbouring individuals have a direct effect on each other. The individuals are distributed in grid where each cell contains a single individual. (Sadly?) It is no different than the list implementation of the bag population other that it is composed of lists of individuals.
toolbox.register("row", tools.initRepeat, list, toolbox.individual, n=N_COL) toolbox.register("population", tools.initRepeat, list, toolbox.row, n=N_ROW)
Calling :func:`toolbox.population` will readily return a complete population
where the individuals are accessible using two indices for example
pop[r][c]
. For the moment there is no algorithm specialized for structured
population, we are waiting your submissions.
A swarm is used in particle swarm optimization, it is different in the sens that it contains a network of communication. The simplest network is the complete connection where each particle knows the best position of that ever been visited by any other particle. This is generally implemented by copying that global best position to a :attr:`gbest` attribute and the global best fitness to a :attr:`gbestfit` attribute.
creator.create("Swarm", list, gbest=None, gbestfit=creator.FitnessMax) toolbox.register("swarm", tools.initRepeat, creator.Swarm, toolbox.particle)
Calling :func:`toolbox.population` will readily return a complete swarm. After each evaluation the :attr:`gbest` and :attr:`gbestfit` are set to reflect the best found position and fitness.
A deme is a sub-population that is contained in a population. It is similar has an island in the island model. Demes being only sub-population are in fact no different than population other than by their names. Here we create a population containing 3 demes each having a different number of individuals using the n argument of the :func:`~deap.tools.initRepeat` function.
toolbox.register("deme", tools.initRepeat, list, toolbox.individual) DEME_SIZES = 10, 50, 100 population = [toolbox.deme(n=i) for i in DEME_SIZES]